A winter sports video classification method and system based on 3D-video Swin transformer

Xiaolin Yuan

doi:10.1117/12.2678658

14 April 2023 A winter sports video classification method and system based on 3D-video Swin transformer

Xiaolin Yuan

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340R (2023) https://doi.org/10.1117/12.2678658
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

Videos of winter sports can be a very good resource for learning about sports in China. In this paper, we address the issues of a small winter sports video dataset and low classification accuracy by proposing a fast video classification approach that combines a transformer and a convolutional neural network. The 3D-Video Swin Transformer model is built using the resnet3D feature extraction component and the Video Swin Transformer, and it improves local and global modeling capabilities through a multi-headed self-attention mechanism. Convolutional operations are used at the network front-end to make for the Transformer's lack of inductive bias, enhancing the network's local modeling capabilities. Convolution operations are utilized at the network's front end to make for Transformer's lack of inductive bias, hence increasing the network's capacity for local modeling and decreasing the model's dependency on vast amounts of data. The experimental results indicate that the 3D-Video Swin Transformer model may achieve an accuracy of up to 76.43 percent on the winter sports video dataset developed in this paper. The classification impact is also substantially stronger, and this accuracy is also 1.15 percent higher than that of the Video Swin Transformer Network. Additionally, we develop and implement a winter sports video classification system based on the Milvus database to facilitate user interaction and enable the submission, categorization, and recommendation of winter sports movies.

Citation Download Citation

Xiaolin Yuan "A winter sports video classification method and system based on 3D-video Swin transformer", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340R (14 April 2023); https://doi.org/10.1117/12.2678658

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Video

Transformers

3D modeling

Data modeling

Convolutional neural networks

Education and training

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years